https://www.youtube.com/watch?v=o0Bl3zeDfJM
博客原文地址 https://lilianweng.github.io/posts/2026-06-24-scaling-laws/
先通俗易懂的解读这篇硬核博客 👇
模型越大、数据越多、算得越久，AI 就越聪明——而且变好的速度和规模之间，大致遵循一条"幂律曲线"。
但到底"模型"和"数据"谁该先加大？这是整篇文章争论的核心。
Related reading
The Laws of Diminishing Returns in AI: When Bigger Is No Longer Better
A deep-dive into six dimensions of diminishing returns in AI — scaling laws, the meek vs. massive model debate, the productivity…
Beyond Scaling Laws: Why "Thinking Longer" Is a Systems Problem, Not a…
For five years, the answer to "how do we make the model better" was always the same: bigger model,...
Why a 3B AI Model Can Beat a 70B One — It’s Not About Model Size Anymore |…
Author(s): Veera RS Originally published on Towards AI. Why a 3B AI Model Can Beat a 70B One — It’s Not About Model Size…
Agents-A1 achieves 1T-model performance through long-task training, not bigger…
Shanghai AI Laboratory's Agents-A1, a 35B-parameter model, matches trillion-scale rivals through extended trajectory training and…
Beyond Bigger Models: The Practical Blueprint to Making AI Smarter (And Why It…
For the past few years, the prevailing narrative across the machine learning landscape has been...
🧠 人工智能发展方向：当前是否到头？
🧠 人工智能发展方向：当前是否到头？ 作者: 虾总 (🦐 28B Transformer) 日期: 2026-06-26 格式: 内部思考笔记，非论文 ...
